Abstract

This article describes a method for efficient posterior simulation for Bayesian variable selection in probit regression models with many regressors but few observations.A proposal on model space is described which contains a tuneable parameter. Anadaptive approach to choosing this tuning parameter is described which allows automatic, e±cient computation in these models. The methods is applied to the analysisof gene expression data.